State of the Product Data

The data for this report was pulled via the NetSuite saved search “product-data-fields-trimmed” on 2024-01-31.

Note that all sections of this report exclude the following sets of records:

• Inactive Records
• Products from vendors no longer with SEFA, namely Rubbermaid and Hollowick

Also note that Closeout items are treated here as belonging to the “Vendor” Closeouts.

1 Display in Web Site Readiness

Here are summaries by supplier details pertinent to products eligible to be live on the site by setting the value of Display in Web Site to Manufacturer65. These counts may include products already live on the site.

1.1 Product Data Readiness

# for (i in 1:6) {
#   run_report(item_health_template, ns_dat_list[[i]], paste0("### ", names(ns_dat_list)[i]))
# }
run_report(item_health_template, ns_dat_list[[1]], paste0("### ", names(ns_dat_list)[1]))

1.1.1 All of NetSuite

dat %>%
  select(-any_of("WRA_prob_list")) %>% 
  select(robs_vendor_name, contains("WRA_")) %>%
  mutate(sum = rowSums(across(where(is.logical)))) %>%
  group_by(robs_vendor_name) %>%
  summarise(`Overall Data Readiness Percentage` = mean(sum) / length(wra_names)) %>%
  select(robs_vendor_name, `Overall Data Readiness Percentage`) %>%
  identity() -> overall_readiness_lookup_df

dat %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    readiness_alignment = c("opportunity", "liability"),
    n = c("0", "0")
    )) %>% 
  count(robs_vendor_name, readiness_alignment) %>% 
  mutate(
    readiness_alignment = readiness_alignment %>% 
      str_to_lower %>% 
      str_replace(" ", "_") 
    ) %>% 
  pivot_wider(names_from = readiness_alignment, values_from = n, values_fill = 0) %>% 
  identity() -> readiness_alignment_lookup_df

dat %>% 
  count(robs_vendor_name, 
        DIWS_ready_counts = ifelse(DIWS_ready, "DIWS Ready", "Not DIWS Ready")) %>%
  left_join(overall_readiness_lookup_df) %>% 
  left_join({
    dat %>% 
      filter(`Display in Web Site` == "Yes") %>% 
      count(robs_vendor_name, name = "Live")
  }) %>% 
  mutate(across(everything(), as.character)) %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    DIWS_ready_counts = c("DIWS Ready", "Not DIWS Ready"),
    n = c("0", "0")
    )) %>% 
  mutate(across(everything(), parse_guess)) %>% 
  pivot_wider(names_from = DIWS_ready_counts, values_from = n, values_fill = 0) %>%
  mutate(
    `Total Product Count` = `DIWS Ready` + `Not DIWS Ready`,
    `DIWS Ready %` = `DIWS Ready` / `Total Product Count`,
    Live = case_when(!is.na(Live) ~ Live, Manufacturer65 ~ 0),
    `Live %` = Live / `Total Product Count`
    ) %>%
  left_join(readiness_alignment_lookup_df) %>% 
  filter(robs_vendor_name != "fake") %>% 
  identity() -> ready_df

dat %>% 
  select(-any_of("WRA_prob_list")) %>% 
  filter(!DIWS_ready) %>%
  select(Name, robs_vendor_name, contains("WRA_")) %>%
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  pivot_longer(-c(Name, robs_vendor_name), names_to = "assessment", values_to = "assessment_is_good") %>%
  filter(!assessment_is_good) %>%
  count(robs_vendor_name, assessment, sort = T) %>%
  mutate(prob_counts = paste0(assessment, "_", n)) %>%
  select(-assessment)%>%
  group_by(robs_vendor_name) %>%
  summarise(
    prob_count = sum(n),
    prob_type_count = n(),
    count_type_ratio = (prob_count / prob_type_count) %>% round(2),
    prob_list = paste0(prob_counts, collapse = ", ")
    ) %>%
  full_join(ready_df) %>% 
  select(
    Vendor = robs_vendor_name,
    `Total Product Count`,
    Live, `Live %`, 
    `Opportunity Count` = opportunity,
    `Liability Count` = liability,
    `DIWS Product Ready Count` = `DIWS Ready`,
    `DIWS Product Ready Percentage` = `DIWS Ready %`,
    `Not DIWS Ready`,
    `Overall Data Readiness Percentage`,
    `Problem Value Count` =  prob_count,
    `Problem Type Count` =  prob_type_count,
    `Problem List` = prob_list
  ) %>% 
  # filter(Vendor == "Manufacturer63")
  identity() -> table_to_print

col_hide_list <- c("Live", "Live %", "Opportunity Count", "Liability Count", "Problem List")
col_hide_targets <- which(colnames(table_to_print) %in% col_hide_list) - 1

table_to_print %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    ) %>% 
  formatPercentage(c(
    "Live %",
    "DIWS Product Ready Percentage",
    "Overall Data Readiness Percentage"
    ), 1) %>%
  formatStyle("DIWS Product Ready Percentage",
              background = styleColorBar(
                table_to_print$`DIWS Product Ready Percentage`, 'lightgreen'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right') %>%
  formatStyle("Overall Data Readiness Percentage",
              background = styleColorBar(
                table_to_print$`Overall Data Readiness Percentage`, 'lightblue'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right')

run_report(item_health_template, ns_dat_list[[2]], paste0("### ", names(ns_dat_list)[2]))

1.1.2 Live Items

dat %>%
  select(-any_of("WRA_prob_list")) %>% 
  select(robs_vendor_name, contains("WRA_")) %>%
  mutate(sum = rowSums(across(where(is.logical)))) %>%
  group_by(robs_vendor_name) %>%
  summarise(`Overall Data Readiness Percentage` = mean(sum) / length(wra_names)) %>%
  select(robs_vendor_name, `Overall Data Readiness Percentage`) %>%
  identity() -> overall_readiness_lookup_df

dat %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    readiness_alignment = c("opportunity", "liability"),
    n = c("0", "0")
    )) %>% 
  count(robs_vendor_name, readiness_alignment) %>% 
  mutate(
    readiness_alignment = readiness_alignment %>% 
      str_to_lower %>% 
      str_replace(" ", "_") 
    ) %>% 
  pivot_wider(names_from = readiness_alignment, values_from = n, values_fill = 0) %>% 
  identity() -> readiness_alignment_lookup_df

dat %>% 
  count(robs_vendor_name, 
        DIWS_ready_counts = ifelse(DIWS_ready, "DIWS Ready", "Not DIWS Ready")) %>%
  left_join(overall_readiness_lookup_df) %>% 
  left_join({
    dat %>% 
      filter(`Display in Web Site` == "Yes") %>% 
      count(robs_vendor_name, name = "Live")
  }) %>% 
  mutate(across(everything(), as.character)) %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    DIWS_ready_counts = c("DIWS Ready", "Not DIWS Ready"),
    n = c("0", "0")
    )) %>% 
  mutate(across(everything(), parse_guess)) %>% 
  pivot_wider(names_from = DIWS_ready_counts, values_from = n, values_fill = 0) %>%
  mutate(
    `Total Product Count` = `DIWS Ready` + `Not DIWS Ready`,
    `DIWS Ready %` = `DIWS Ready` / `Total Product Count`,
    Live = case_when(!is.na(Live) ~ Live, Manufacturer65 ~ 0),
    `Live %` = Live / `Total Product Count`
    ) %>%
  left_join(readiness_alignment_lookup_df) %>% 
  filter(robs_vendor_name != "fake") %>% 
  identity() -> ready_df

dat %>% 
  select(-any_of("WRA_prob_list")) %>% 
  filter(!DIWS_ready) %>%
  select(Name, robs_vendor_name, contains("WRA_")) %>%
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  pivot_longer(-c(Name, robs_vendor_name), names_to = "assessment", values_to = "assessment_is_good") %>%
  filter(!assessment_is_good) %>%
  count(robs_vendor_name, assessment, sort = T) %>%
  mutate(prob_counts = paste0(assessment, "_", n)) %>%
  select(-assessment)%>%
  group_by(robs_vendor_name) %>%
  summarise(
    prob_count = sum(n),
    prob_type_count = n(),
    count_type_ratio = (prob_count / prob_type_count) %>% round(2),
    prob_list = paste0(prob_counts, collapse = ", ")
    ) %>%
  full_join(ready_df) %>% 
  select(
    Vendor = robs_vendor_name,
    `Total Product Count`,
    Live, `Live %`, 
    `Opportunity Count` = opportunity,
    `Liability Count` = liability,
    `DIWS Product Ready Count` = `DIWS Ready`,
    `DIWS Product Ready Percentage` = `DIWS Ready %`,
    `Not DIWS Ready`,
    `Overall Data Readiness Percentage`,
    `Problem Value Count` =  prob_count,
    `Problem Type Count` =  prob_type_count,
    `Problem List` = prob_list
  ) %>% 
  # filter(Vendor == "Manufacturer63")
  identity() -> table_to_print

col_hide_list <- c("Live", "Live %", "Opportunity Count", "Liability Count", "Problem List")
col_hide_targets <- which(colnames(table_to_print) %in% col_hide_list) - 1

table_to_print %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    ) %>% 
  formatPercentage(c(
    "Live %",
    "DIWS Product Ready Percentage",
    "Overall Data Readiness Percentage"
    ), 1) %>%
  formatStyle("DIWS Product Ready Percentage",
              background = styleColorBar(
                table_to_print$`DIWS Product Ready Percentage`, 'lightgreen'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right') %>%
  formatStyle("Overall Data Readiness Percentage",
              background = styleColorBar(
                table_to_print$`Overall Data Readiness Percentage`, 'lightblue'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right')

run_report(item_health_template, ns_dat_list[[3]], paste0("### ", names(ns_dat_list)[3]))

1.1.3 Live Dropship Items

dat %>%
  select(-any_of("WRA_prob_list")) %>% 
  select(robs_vendor_name, contains("WRA_")) %>%
  mutate(sum = rowSums(across(where(is.logical)))) %>%
  group_by(robs_vendor_name) %>%
  summarise(`Overall Data Readiness Percentage` = mean(sum) / length(wra_names)) %>%
  select(robs_vendor_name, `Overall Data Readiness Percentage`) %>%
  identity() -> overall_readiness_lookup_df

dat %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    readiness_alignment = c("opportunity", "liability"),
    n = c("0", "0")
    )) %>% 
  count(robs_vendor_name, readiness_alignment) %>% 
  mutate(
    readiness_alignment = readiness_alignment %>% 
      str_to_lower %>% 
      str_replace(" ", "_") 
    ) %>% 
  pivot_wider(names_from = readiness_alignment, values_from = n, values_fill = 0) %>% 
  identity() -> readiness_alignment_lookup_df

dat %>% 
  count(robs_vendor_name, 
        DIWS_ready_counts = ifelse(DIWS_ready, "DIWS Ready", "Not DIWS Ready")) %>%
  left_join(overall_readiness_lookup_df) %>% 
  left_join({
    dat %>% 
      filter(`Display in Web Site` == "Yes") %>% 
      count(robs_vendor_name, name = "Live")
  }) %>% 
  mutate(across(everything(), as.character)) %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    DIWS_ready_counts = c("DIWS Ready", "Not DIWS Ready"),
    n = c("0", "0")
    )) %>% 
  mutate(across(everything(), parse_guess)) %>% 
  pivot_wider(names_from = DIWS_ready_counts, values_from = n, values_fill = 0) %>%
  mutate(
    `Total Product Count` = `DIWS Ready` + `Not DIWS Ready`,
    `DIWS Ready %` = `DIWS Ready` / `Total Product Count`,
    Live = case_when(!is.na(Live) ~ Live, Manufacturer65 ~ 0),
    `Live %` = Live / `Total Product Count`
    ) %>%
  left_join(readiness_alignment_lookup_df) %>% 
  filter(robs_vendor_name != "fake") %>% 
  identity() -> ready_df

dat %>% 
  select(-any_of("WRA_prob_list")) %>% 
  filter(!DIWS_ready) %>%
  select(Name, robs_vendor_name, contains("WRA_")) %>%
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  pivot_longer(-c(Name, robs_vendor_name), names_to = "assessment", values_to = "assessment_is_good") %>%
  filter(!assessment_is_good) %>%
  count(robs_vendor_name, assessment, sort = T) %>%
  mutate(prob_counts = paste0(assessment, "_", n)) %>%
  select(-assessment)%>%
  group_by(robs_vendor_name) %>%
  summarise(
    prob_count = sum(n),
    prob_type_count = n(),
    count_type_ratio = (prob_count / prob_type_count) %>% round(2),
    prob_list = paste0(prob_counts, collapse = ", ")
    ) %>%
  full_join(ready_df) %>% 
  select(
    Vendor = robs_vendor_name,
    `Total Product Count`,
    Live, `Live %`, 
    `Opportunity Count` = opportunity,
    `Liability Count` = liability,
    `DIWS Product Ready Count` = `DIWS Ready`,
    `DIWS Product Ready Percentage` = `DIWS Ready %`,
    `Not DIWS Ready`,
    `Overall Data Readiness Percentage`,
    `Problem Value Count` =  prob_count,
    `Problem Type Count` =  prob_type_count,
    `Problem List` = prob_list
  ) %>% 
  # filter(Vendor == "Manufacturer63")
  identity() -> table_to_print

col_hide_list <- c("Live", "Live %", "Opportunity Count", "Liability Count", "Problem List")
col_hide_targets <- which(colnames(table_to_print) %in% col_hide_list) - 1

table_to_print %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    ) %>% 
  formatPercentage(c(
    "Live %",
    "DIWS Product Ready Percentage",
    "Overall Data Readiness Percentage"
    ), 1) %>%
  formatStyle("DIWS Product Ready Percentage",
              background = styleColorBar(
                table_to_print$`DIWS Product Ready Percentage`, 'lightgreen'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right') %>%
  formatStyle("Overall Data Readiness Percentage",
              background = styleColorBar(
                table_to_print$`Overall Data Readiness Percentage`, 'lightblue'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right')

run_report(item_health_template, ns_dat_list[[4]], paste0("### ", names(ns_dat_list)[4]))

1.1.4 Live Dealer Items

dat %>%
  select(-any_of("WRA_prob_list")) %>% 
  select(robs_vendor_name, contains("WRA_")) %>%
  mutate(sum = rowSums(across(where(is.logical)))) %>%
  group_by(robs_vendor_name) %>%
  summarise(`Overall Data Readiness Percentage` = mean(sum) / length(wra_names)) %>%
  select(robs_vendor_name, `Overall Data Readiness Percentage`) %>%
  identity() -> overall_readiness_lookup_df

dat %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    readiness_alignment = c("opportunity", "liability"),
    n = c("0", "0")
    )) %>% 
  count(robs_vendor_name, readiness_alignment) %>% 
  mutate(
    readiness_alignment = readiness_alignment %>% 
      str_to_lower %>% 
      str_replace(" ", "_") 
    ) %>% 
  pivot_wider(names_from = readiness_alignment, values_from = n, values_fill = 0) %>% 
  identity() -> readiness_alignment_lookup_df

dat %>% 
  count(robs_vendor_name, 
        DIWS_ready_counts = ifelse(DIWS_ready, "DIWS Ready", "Not DIWS Ready")) %>%
  left_join(overall_readiness_lookup_df) %>% 
  left_join({
    dat %>% 
      filter(`Display in Web Site` == "Yes") %>% 
      count(robs_vendor_name, name = "Live")
  }) %>% 
  mutate(across(everything(), as.character)) %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    DIWS_ready_counts = c("DIWS Ready", "Not DIWS Ready"),
    n = c("0", "0")
    )) %>% 
  mutate(across(everything(), parse_guess)) %>% 
  pivot_wider(names_from = DIWS_ready_counts, values_from = n, values_fill = 0) %>%
  mutate(
    `Total Product Count` = `DIWS Ready` + `Not DIWS Ready`,
    `DIWS Ready %` = `DIWS Ready` / `Total Product Count`,
    Live = case_when(!is.na(Live) ~ Live, Manufacturer65 ~ 0),
    `Live %` = Live / `Total Product Count`
    ) %>%
  left_join(readiness_alignment_lookup_df) %>% 
  filter(robs_vendor_name != "fake") %>% 
  identity() -> ready_df

dat %>% 
  select(-any_of("WRA_prob_list")) %>% 
  filter(!DIWS_ready) %>%
  select(Name, robs_vendor_name, contains("WRA_")) %>%
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  pivot_longer(-c(Name, robs_vendor_name), names_to = "assessment", values_to = "assessment_is_good") %>%
  filter(!assessment_is_good) %>%
  count(robs_vendor_name, assessment, sort = T) %>%
  mutate(prob_counts = paste0(assessment, "_", n)) %>%
  select(-assessment)%>%
  group_by(robs_vendor_name) %>%
  summarise(
    prob_count = sum(n),
    prob_type_count = n(),
    count_type_ratio = (prob_count / prob_type_count) %>% round(2),
    prob_list = paste0(prob_counts, collapse = ", ")
    ) %>%
  full_join(ready_df) %>% 
  select(
    Vendor = robs_vendor_name,
    `Total Product Count`,
    Live, `Live %`, 
    `Opportunity Count` = opportunity,
    `Liability Count` = liability,
    `DIWS Product Ready Count` = `DIWS Ready`,
    `DIWS Product Ready Percentage` = `DIWS Ready %`,
    `Not DIWS Ready`,
    `Overall Data Readiness Percentage`,
    `Problem Value Count` =  prob_count,
    `Problem Type Count` =  prob_type_count,
    `Problem List` = prob_list
  ) %>% 
  # filter(Vendor == "Manufacturer63")
  identity() -> table_to_print

col_hide_list <- c("Live", "Live %", "Opportunity Count", "Liability Count", "Problem List")
col_hide_targets <- which(colnames(table_to_print) %in% col_hide_list) - 1

table_to_print %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    ) %>% 
  formatPercentage(c(
    "Live %",
    "DIWS Product Ready Percentage",
    "Overall Data Readiness Percentage"
    ), 1) %>%
  formatStyle("DIWS Product Ready Percentage",
              background = styleColorBar(
                table_to_print$`DIWS Product Ready Percentage`, 'lightgreen'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right') %>%
  formatStyle("Overall Data Readiness Percentage",
              background = styleColorBar(
                table_to_print$`Overall Data Readiness Percentage`, 'lightblue'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right')

run_report(item_health_template, ns_dat_list[[5]], paste0("### ", names(ns_dat_list)[5]))

1.1.5 Items in Progress

dat %>%
  select(-any_of("WRA_prob_list")) %>% 
  select(robs_vendor_name, contains("WRA_")) %>%
  mutate(sum = rowSums(across(where(is.logical)))) %>%
  group_by(robs_vendor_name) %>%
  summarise(`Overall Data Readiness Percentage` = mean(sum) / length(wra_names)) %>%
  select(robs_vendor_name, `Overall Data Readiness Percentage`) %>%
  identity() -> overall_readiness_lookup_df

dat %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    readiness_alignment = c("opportunity", "liability"),
    n = c("0", "0")
    )) %>% 
  count(robs_vendor_name, readiness_alignment) %>% 
  mutate(
    readiness_alignment = readiness_alignment %>% 
      str_to_lower %>% 
      str_replace(" ", "_") 
    ) %>% 
  pivot_wider(names_from = readiness_alignment, values_from = n, values_fill = 0) %>% 
  identity() -> readiness_alignment_lookup_df

dat %>% 
  count(robs_vendor_name, 
        DIWS_ready_counts = ifelse(DIWS_ready, "DIWS Ready", "Not DIWS Ready")) %>%
  left_join(overall_readiness_lookup_df) %>% 
  left_join({
    dat %>% 
      filter(`Display in Web Site` == "Yes") %>% 
      count(robs_vendor_name, name = "Live")
  }) %>% 
  mutate(across(everything(), as.character)) %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    DIWS_ready_counts = c("DIWS Ready", "Not DIWS Ready"),
    n = c("0", "0")
    )) %>% 
  mutate(across(everything(), parse_guess)) %>% 
  pivot_wider(names_from = DIWS_ready_counts, values_from = n, values_fill = 0) %>%
  mutate(
    `Total Product Count` = `DIWS Ready` + `Not DIWS Ready`,
    `DIWS Ready %` = `DIWS Ready` / `Total Product Count`,
    Live = case_when(!is.na(Live) ~ Live, Manufacturer65 ~ 0),
    `Live %` = Live / `Total Product Count`
    ) %>%
  left_join(readiness_alignment_lookup_df) %>% 
  filter(robs_vendor_name != "fake") %>% 
  identity() -> ready_df

dat %>% 
  select(-any_of("WRA_prob_list")) %>% 
  filter(!DIWS_ready) %>%
  select(Name, robs_vendor_name, contains("WRA_")) %>%
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  pivot_longer(-c(Name, robs_vendor_name), names_to = "assessment", values_to = "assessment_is_good") %>%
  filter(!assessment_is_good) %>%
  count(robs_vendor_name, assessment, sort = T) %>%
  mutate(prob_counts = paste0(assessment, "_", n)) %>%
  select(-assessment)%>%
  group_by(robs_vendor_name) %>%
  summarise(
    prob_count = sum(n),
    prob_type_count = n(),
    count_type_ratio = (prob_count / prob_type_count) %>% round(2),
    prob_list = paste0(prob_counts, collapse = ", ")
    ) %>%
  full_join(ready_df) %>% 
  select(
    Vendor = robs_vendor_name,
    `Total Product Count`,
    Live, `Live %`, 
    `Opportunity Count` = opportunity,
    `Liability Count` = liability,
    `DIWS Product Ready Count` = `DIWS Ready`,
    `DIWS Product Ready Percentage` = `DIWS Ready %`,
    `Not DIWS Ready`,
    `Overall Data Readiness Percentage`,
    `Problem Value Count` =  prob_count,
    `Problem Type Count` =  prob_type_count,
    `Problem List` = prob_list
  ) %>% 
  # filter(Vendor == "Manufacturer63")
  identity() -> table_to_print

col_hide_list <- c("Live", "Live %", "Opportunity Count", "Liability Count", "Problem List")
col_hide_targets <- which(colnames(table_to_print) %in% col_hide_list) - 1

table_to_print %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    ) %>% 
  formatPercentage(c(
    "Live %",
    "DIWS Product Ready Percentage",
    "Overall Data Readiness Percentage"
    ), 1) %>%
  formatStyle("DIWS Product Ready Percentage",
              background = styleColorBar(
                table_to_print$`DIWS Product Ready Percentage`, 'lightgreen'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right') %>%
  formatStyle("Overall Data Readiness Percentage",
              background = styleColorBar(
                table_to_print$`Overall Data Readiness Percentage`, 'lightblue'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right')

run_report(item_health_template, ns_dat_list[[6]], paste0("### ", names(ns_dat_list)[6]))

1.1.6 Liabilities

dat %>%
  select(-any_of("WRA_prob_list")) %>% 
  select(robs_vendor_name, contains("WRA_")) %>%
  mutate(sum = rowSums(across(where(is.logical)))) %>%
  group_by(robs_vendor_name) %>%
  summarise(`Overall Data Readiness Percentage` = mean(sum) / length(wra_names)) %>%
  select(robs_vendor_name, `Overall Data Readiness Percentage`) %>%
  identity() -> overall_readiness_lookup_df

dat %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    readiness_alignment = c("opportunity", "liability"),
    n = c("0", "0")
    )) %>% 
  count(robs_vendor_name, readiness_alignment) %>% 
  mutate(
    readiness_alignment = readiness_alignment %>% 
      str_to_lower %>% 
      str_replace(" ", "_") 
    ) %>% 
  pivot_wider(names_from = readiness_alignment, values_from = n, values_fill = 0) %>% 
  identity() -> readiness_alignment_lookup_df

dat %>% 
  count(robs_vendor_name, 
        DIWS_ready_counts = ifelse(DIWS_ready, "DIWS Ready", "Not DIWS Ready")) %>%
  left_join(overall_readiness_lookup_df) %>% 
  left_join({
    dat %>% 
      filter(`Display in Web Site` == "Yes") %>% 
      count(robs_vendor_name, name = "Live")
  }) %>% 
  mutate(across(everything(), as.character)) %>% 
  bind_rows(tibble(
    robs_vendor_name = c("fake", "fake"),
    DIWS_ready_counts = c("DIWS Ready", "Not DIWS Ready"),
    n = c("0", "0")
    )) %>% 
  mutate(across(everything(), parse_guess)) %>% 
  pivot_wider(names_from = DIWS_ready_counts, values_from = n, values_fill = 0) %>%
  mutate(
    `Total Product Count` = `DIWS Ready` + `Not DIWS Ready`,
    `DIWS Ready %` = `DIWS Ready` / `Total Product Count`,
    Live = case_when(!is.na(Live) ~ Live, Manufacturer65 ~ 0),
    `Live %` = Live / `Total Product Count`
    ) %>%
  left_join(readiness_alignment_lookup_df) %>% 
  filter(robs_vendor_name != "fake") %>% 
  identity() -> ready_df

dat %>% 
  select(-any_of("WRA_prob_list")) %>% 
  filter(!DIWS_ready) %>%
  select(Name, robs_vendor_name, contains("WRA_")) %>%
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  pivot_longer(-c(Name, robs_vendor_name), names_to = "assessment", values_to = "assessment_is_good") %>%
  filter(!assessment_is_good) %>%
  count(robs_vendor_name, assessment, sort = T) %>%
  mutate(prob_counts = paste0(assessment, "_", n)) %>%
  select(-assessment)%>%
  group_by(robs_vendor_name) %>%
  summarise(
    prob_count = sum(n),
    prob_type_count = n(),
    count_type_ratio = (prob_count / prob_type_count) %>% round(2),
    prob_list = paste0(prob_counts, collapse = ", ")
    ) %>%
  full_join(ready_df) %>% 
  select(
    Vendor = robs_vendor_name,
    `Total Product Count`,
    Live, `Live %`, 
    `Opportunity Count` = opportunity,
    `Liability Count` = liability,
    `DIWS Product Ready Count` = `DIWS Ready`,
    `DIWS Product Ready Percentage` = `DIWS Ready %`,
    `Not DIWS Ready`,
    `Overall Data Readiness Percentage`,
    `Problem Value Count` =  prob_count,
    `Problem Type Count` =  prob_type_count,
    `Problem List` = prob_list
  ) %>% 
  # filter(Vendor == "Manufacturer63")
  identity() -> table_to_print

col_hide_list <- c("Live", "Live %", "Opportunity Count", "Liability Count", "Problem List")
col_hide_targets <- which(colnames(table_to_print) %in% col_hide_list) - 1

table_to_print %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    ) %>% 
  formatPercentage(c(
    "Live %",
    "DIWS Product Ready Percentage",
    "Overall Data Readiness Percentage"
    ), 1) %>%
  formatStyle("DIWS Product Ready Percentage",
              background = styleColorBar(
                table_to_print$`DIWS Product Ready Percentage`, 'lightgreen'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right') %>%
  formatStyle("Overall Data Readiness Percentage",
              background = styleColorBar(
                table_to_print$`Overall Data Readiness Percentage`, 'lightblue'),
              backgroundSize = '95% 50%',
              backgroundRepeat = 'no-repeat',
              backgroundPosition = 'right')

# run_report(item_health_template, ns_dat_list[[6]], paste0("### ", names(ns_dat_list)[7]))

1.1.7 DiWS Criteria

assessment_name_lookup_df <- tribble(
  ~tidied_name, ~table_label,
  "FAIL_PRI_pref_eq_pur", "Preferred Price fails to be equal to Purchase Price",
  "FAIL_PRI_base_gte_pur", "Base Price fails to be greater than or equal to Purchase Price",
  "FAIL_PRI_base_nna", "Base Price fails to be populated",
  "FAIL_PRI_sdb_meta_nna", "SDB | Price Meta fails to be populated",
  "FAIL_PRI_uoms_nna_match", "UOMs fail to be populated or fail to match",
  "FAIL_PRI_cul_nnfs", "Specially fulfilled item is Not For Sale or has a Reason",
  "FAIL_SHP_dims_nna", "Item fails to have all four shipping dimensions populated",
  "FAIL_CNT_dn_fam_match", "The Display Name fields do not all match",
  "FAIL_CNT_bull_nna", "Bullets are missing",
  # "FAIL_CNT_desc_nna", "Long Description is missing",
  # "FAIL_CAT_com_nna", "Commerce Categories are missing",
  # "FAIL_CAT_fac_nna", "Facet Categories are missing",
  # "FAIL_CAT_align", "Commerce and Facet Categories are misaligned",
  "FAIL_min_qty_nna_gt0", "Minimum Quantity fails to be greater than zero",
  "FAIL_avatax_nna", "AvaTax Taxcode is missing",
  "FAIL_vendor_nna", "Vendor is missing",
  "FAIL_vendor_name_nna", "Vendor Name is missing",
  "FAIL_has_image", "Item fails to have any images in NetSuite"
)
# cat("Here are the criteria that may make a record fail to qualify as Display-in-Web-Site-Ready.\n")
cat(c(
  "Here are the criteria that may make a record fail to qualify as Display-in-Web-Site-Ready.\n",
  paste('-', assessment_name_lookup_df$table_label)), sep = '\n')

Here are the criteria that may make a record fail to qualify as Display-in-Web-Site-Ready.

• Preferred Price fails to be equal to Purchase Price
• Base Price fails to be greater than or equal to Purchase Price
• Base Price fails to be populated
• SDB | Price Meta fails to be populated
• UOMs fail to be populated or fail to match
• Specially fulfilled item is Not For Sale or has a Reason
• Item fails to have all four shipping dimensions populated
• The Display Name fields do not all match
• Bullets are missing
• Minimum Quantity fails to be greater than zero
• AvaTax Taxcode is missing
• Vendor is missing
• Vendor Name is missing
• Item fails to have any images in NetSuite

1.2 Finer Assessments

run_report(item_health_finer_template, ns_dat_list[[1]], paste0("### ", names(ns_dat_list)[1]))

1.2.1 All of NetSuite

assessment_name_lookup_df <- tribble(
  ~tidied_name, ~table_label,
  "FAIL_PRI_pref_eq_pur", "Preferred Price fails to be equal to Purchase Price",
  "FAIL_PRI_base_gte_pur", "Base Price fails to be greater than or equal to Purchase Price",
  "FAIL_PRI_base_nna", "Base Price fails to be populated",
  # "FAIL_PRI_sdb_meta_nna", "SDB | Price Meta fails to be populated",
  "FAIL_PRI_mcr_nna", "Marketplace Commission Rate fails to be populated",
  "FAIL_PRI_uoms_nna_match", "UOMs fail to be populated or fail to match",
  "FAIL_PRI_cul_nnfs", "Specially fulfilled item is Not For Sale or has a Reason",
  "FAIL_SHP_dims_nna", "Item fails to have all four shipping dimensions populated",
  "FAIL_CNT_dn_fam_match", "The Display Name fields do not all match",
  "FAIL_CNT_bull_nna", "Bullets are missing",
  # "FAIL_CNT_desc_nna", "Long Description is missing",
  # "FAIL_CAT_com_nna", "Commerce Categories are missing",
  # "FAIL_CAT_fac_nna", "Facet Categories are missing",
  # "FAIL_CAT_align", "Commerce and Facet Categories are misaligned",
  "FAIL_min_qty_nna_gt0", "Minimum Quantity fails to be greater than zero",
  "FAIL_avatax_nna", "AvaTax Taxcode is missing",
  "FAIL_vendor_nna", "Vendor is missing",
  "FAIL_vendor_name_nna", "Vendor Name is missing",
  "FAIL_has_image", "Item fails to have any images in NetSuite"
)

dat %>%
  group_by(robs_vendor_name) %>%
  # select(contains("WRA_")) %>% head %>% view
  select(-any_of("WRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("WRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("WRA_")) %>% 
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  select(robs_vendor_name, `Total Records`, all_of(assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

run_report(item_health_finer_template, ns_dat_list[[2]], paste0("### ", names(ns_dat_list)[2]))

1.2.2 Live Items

assessment_name_lookup_df <- tribble(
  ~tidied_name, ~table_label,
  "FAIL_PRI_pref_eq_pur", "Preferred Price fails to be equal to Purchase Price",
  "FAIL_PRI_base_gte_pur", "Base Price fails to be greater than or equal to Purchase Price",
  "FAIL_PRI_base_nna", "Base Price fails to be populated",
  # "FAIL_PRI_sdb_meta_nna", "SDB | Price Meta fails to be populated",
  "FAIL_PRI_mcr_nna", "Marketplace Commission Rate fails to be populated",
  "FAIL_PRI_uoms_nna_match", "UOMs fail to be populated or fail to match",
  "FAIL_PRI_cul_nnfs", "Specially fulfilled item is Not For Sale or has a Reason",
  "FAIL_SHP_dims_nna", "Item fails to have all four shipping dimensions populated",
  "FAIL_CNT_dn_fam_match", "The Display Name fields do not all match",
  "FAIL_CNT_bull_nna", "Bullets are missing",
  # "FAIL_CNT_desc_nna", "Long Description is missing",
  # "FAIL_CAT_com_nna", "Commerce Categories are missing",
  # "FAIL_CAT_fac_nna", "Facet Categories are missing",
  # "FAIL_CAT_align", "Commerce and Facet Categories are misaligned",
  "FAIL_min_qty_nna_gt0", "Minimum Quantity fails to be greater than zero",
  "FAIL_avatax_nna", "AvaTax Taxcode is missing",
  "FAIL_vendor_nna", "Vendor is missing",
  "FAIL_vendor_name_nna", "Vendor Name is missing",
  "FAIL_has_image", "Item fails to have any images in NetSuite"
)

dat %>%
  group_by(robs_vendor_name) %>%
  # select(contains("WRA_")) %>% head %>% view
  select(-any_of("WRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("WRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("WRA_")) %>% 
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  select(robs_vendor_name, `Total Records`, all_of(assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

run_report(item_health_finer_template, ns_dat_list[[3]], paste0("### ", names(ns_dat_list)[3]))

1.2.3 Live Dropship Items

assessment_name_lookup_df <- tribble(
  ~tidied_name, ~table_label,
  "FAIL_PRI_pref_eq_pur", "Preferred Price fails to be equal to Purchase Price",
  "FAIL_PRI_base_gte_pur", "Base Price fails to be greater than or equal to Purchase Price",
  "FAIL_PRI_base_nna", "Base Price fails to be populated",
  # "FAIL_PRI_sdb_meta_nna", "SDB | Price Meta fails to be populated",
  "FAIL_PRI_mcr_nna", "Marketplace Commission Rate fails to be populated",
  "FAIL_PRI_uoms_nna_match", "UOMs fail to be populated or fail to match",
  "FAIL_PRI_cul_nnfs", "Specially fulfilled item is Not For Sale or has a Reason",
  "FAIL_SHP_dims_nna", "Item fails to have all four shipping dimensions populated",
  "FAIL_CNT_dn_fam_match", "The Display Name fields do not all match",
  "FAIL_CNT_bull_nna", "Bullets are missing",
  # "FAIL_CNT_desc_nna", "Long Description is missing",
  # "FAIL_CAT_com_nna", "Commerce Categories are missing",
  # "FAIL_CAT_fac_nna", "Facet Categories are missing",
  # "FAIL_CAT_align", "Commerce and Facet Categories are misaligned",
  "FAIL_min_qty_nna_gt0", "Minimum Quantity fails to be greater than zero",
  "FAIL_avatax_nna", "AvaTax Taxcode is missing",
  "FAIL_vendor_nna", "Vendor is missing",
  "FAIL_vendor_name_nna", "Vendor Name is missing",
  "FAIL_has_image", "Item fails to have any images in NetSuite"
)

dat %>%
  group_by(robs_vendor_name) %>%
  # select(contains("WRA_")) %>% head %>% view
  select(-any_of("WRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("WRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("WRA_")) %>% 
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  select(robs_vendor_name, `Total Records`, all_of(assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

run_report(item_health_finer_template, ns_dat_list[[4]], paste0("### ", names(ns_dat_list)[4]))

1.2.4 Live Dealer Items

assessment_name_lookup_df <- tribble(
  ~tidied_name, ~table_label,
  "FAIL_PRI_pref_eq_pur", "Preferred Price fails to be equal to Purchase Price",
  "FAIL_PRI_base_gte_pur", "Base Price fails to be greater than or equal to Purchase Price",
  "FAIL_PRI_base_nna", "Base Price fails to be populated",
  # "FAIL_PRI_sdb_meta_nna", "SDB | Price Meta fails to be populated",
  "FAIL_PRI_mcr_nna", "Marketplace Commission Rate fails to be populated",
  "FAIL_PRI_uoms_nna_match", "UOMs fail to be populated or fail to match",
  "FAIL_PRI_cul_nnfs", "Specially fulfilled item is Not For Sale or has a Reason",
  "FAIL_SHP_dims_nna", "Item fails to have all four shipping dimensions populated",
  "FAIL_CNT_dn_fam_match", "The Display Name fields do not all match",
  "FAIL_CNT_bull_nna", "Bullets are missing",
  # "FAIL_CNT_desc_nna", "Long Description is missing",
  # "FAIL_CAT_com_nna", "Commerce Categories are missing",
  # "FAIL_CAT_fac_nna", "Facet Categories are missing",
  # "FAIL_CAT_align", "Commerce and Facet Categories are misaligned",
  "FAIL_min_qty_nna_gt0", "Minimum Quantity fails to be greater than zero",
  "FAIL_avatax_nna", "AvaTax Taxcode is missing",
  "FAIL_vendor_nna", "Vendor is missing",
  "FAIL_vendor_name_nna", "Vendor Name is missing",
  "FAIL_has_image", "Item fails to have any images in NetSuite"
)

dat %>%
  group_by(robs_vendor_name) %>%
  # select(contains("WRA_")) %>% head %>% view
  select(-any_of("WRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("WRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("WRA_")) %>% 
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  select(robs_vendor_name, `Total Records`, all_of(assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

run_report(item_health_finer_template, ns_dat_list[[5]], paste0("### ", names(ns_dat_list)[5]))

1.2.5 Items in Progress

assessment_name_lookup_df <- tribble(
  ~tidied_name, ~table_label,
  "FAIL_PRI_pref_eq_pur", "Preferred Price fails to be equal to Purchase Price",
  "FAIL_PRI_base_gte_pur", "Base Price fails to be greater than or equal to Purchase Price",
  "FAIL_PRI_base_nna", "Base Price fails to be populated",
  # "FAIL_PRI_sdb_meta_nna", "SDB | Price Meta fails to be populated",
  "FAIL_PRI_mcr_nna", "Marketplace Commission Rate fails to be populated",
  "FAIL_PRI_uoms_nna_match", "UOMs fail to be populated or fail to match",
  "FAIL_PRI_cul_nnfs", "Specially fulfilled item is Not For Sale or has a Reason",
  "FAIL_SHP_dims_nna", "Item fails to have all four shipping dimensions populated",
  "FAIL_CNT_dn_fam_match", "The Display Name fields do not all match",
  "FAIL_CNT_bull_nna", "Bullets are missing",
  # "FAIL_CNT_desc_nna", "Long Description is missing",
  # "FAIL_CAT_com_nna", "Commerce Categories are missing",
  # "FAIL_CAT_fac_nna", "Facet Categories are missing",
  # "FAIL_CAT_align", "Commerce and Facet Categories are misaligned",
  "FAIL_min_qty_nna_gt0", "Minimum Quantity fails to be greater than zero",
  "FAIL_avatax_nna", "AvaTax Taxcode is missing",
  "FAIL_vendor_nna", "Vendor is missing",
  "FAIL_vendor_name_nna", "Vendor Name is missing",
  "FAIL_has_image", "Item fails to have any images in NetSuite"
)

dat %>%
  group_by(robs_vendor_name) %>%
  # select(contains("WRA_")) %>% head %>% view
  select(-any_of("WRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("WRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("WRA_")) %>% 
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  select(robs_vendor_name, `Total Records`, all_of(assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

run_report(item_health_finer_template, ns_dat_list[[6]], paste0("### ", names(ns_dat_list)[6]))

1.2.6 Liabilities

assessment_name_lookup_df <- tribble(
  ~tidied_name, ~table_label,
  "FAIL_PRI_pref_eq_pur", "Preferred Price fails to be equal to Purchase Price",
  "FAIL_PRI_base_gte_pur", "Base Price fails to be greater than or equal to Purchase Price",
  "FAIL_PRI_base_nna", "Base Price fails to be populated",
  # "FAIL_PRI_sdb_meta_nna", "SDB | Price Meta fails to be populated",
  "FAIL_PRI_mcr_nna", "Marketplace Commission Rate fails to be populated",
  "FAIL_PRI_uoms_nna_match", "UOMs fail to be populated or fail to match",
  "FAIL_PRI_cul_nnfs", "Specially fulfilled item is Not For Sale or has a Reason",
  "FAIL_SHP_dims_nna", "Item fails to have all four shipping dimensions populated",
  "FAIL_CNT_dn_fam_match", "The Display Name fields do not all match",
  "FAIL_CNT_bull_nna", "Bullets are missing",
  # "FAIL_CNT_desc_nna", "Long Description is missing",
  # "FAIL_CAT_com_nna", "Commerce Categories are missing",
  # "FAIL_CAT_fac_nna", "Facet Categories are missing",
  # "FAIL_CAT_align", "Commerce and Facet Categories are misaligned",
  "FAIL_min_qty_nna_gt0", "Minimum Quantity fails to be greater than zero",
  "FAIL_avatax_nna", "AvaTax Taxcode is missing",
  "FAIL_vendor_nna", "Vendor is missing",
  "FAIL_vendor_name_nna", "Vendor Name is missing",
  "FAIL_has_image", "Item fails to have any images in NetSuite"
)

dat %>%
  group_by(robs_vendor_name) %>%
  # select(contains("WRA_")) %>% head %>% view
  select(-any_of("WRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("WRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("WRA_")) %>% 
  rename_with(~{str_remove(.x, "WRA_")}) %>%
  select(robs_vendor_name, `Total Records`, all_of(assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

# run_report(item_health_finer_template, ns_dat_list[[6]], paste0("### ", names(ns_dat_list)[7]))

2 Google Merchant Center

run_report(gmc_plot_template, ns_dat_list[[1]], paste0("## ", names(ns_dat_list)[1]))

2.1 All of NetSuite

in_NetSuite = nrow(dat)
on_Site = nrow(dat[dat$`Display in Web Site` == "Yes", ])
in_Google_Merch_Feed = nrow(dat[dat$`GMC Feed` == "Yes",])

metrics <- c("In NetSuite", "On Website", "In Google Merchant Center")

gmc_summary_table <- tibble(
  Metric = factor(metrics, levels = metrics),
  `Product Count` = c(in_NetSuite, on_Site, in_Google_Merch_Feed)
)

p <- gmc_summary_table %>% 
  ggplot(aes(x=Metric, y=`Product Count`)) +
  geom_bar(stat="identity", fill = sefa_bars_color)

ggplotly(p)

# run_report(gmc_plot_template, ns_dat_list[[2]], paste0("## ", names(ns_dat_list)[2]))
# run_report(gmc_plot_template, ns_dat_list[[3]], paste0("## ", names(ns_dat_list)[3]))
# run_report(gmc_plot_template, ns_dat_list[[4]], paste0("## ", names(ns_dat_list)[4]))
# run_report(gmc_plot_template, ns_dat_list[[5]], paste0("## ", names(ns_dat_list)[5]))
# run_report(gmc_plot_template, ns_dat_list[[6]], paste0("## ", names(ns_dat_list)[6]))
# run_report(gmc_plot_template, ns_dat_list[[6]], paste0("## ", names(ns_dat_list)[7]))

3 Google Merchant Center Readiness

run_report(gmc_readiness_template, ns_dat_list[[1]], paste0("## ", names(ns_dat_list)[1]))

3.1 All of NetSuite

gmc_assessment_name_lookup_df <- tribble(
  ~tidied_name,             ~table_label,
  "FAIL_dn_le_150",         "Display Name is greater than 150 characters",
  "FAIL_desc_nna",          "Product Details/Short Description Missing",
  "FAIL_cat_nna",           "Google Product Category Missing",
  "FAIL_price_low_enough",  "Price greater than $10000"
)

dat %>%
  group_by(robs_vendor_name) %>%
  select(-any_of("GMCRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("GMCRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("GMCRA_")) %>% 
  rename_with(~{str_remove(.x, "GMCRA_")}) %>%
  select(robs_vendor_name, `Total Records`, any_of(gmc_assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(gmc_assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

run_report(gmc_readiness_template, ns_dat_list[[2]], paste0("## ", names(ns_dat_list)[2]))

3.2 Live Items

gmc_assessment_name_lookup_df <- tribble(
  ~tidied_name,             ~table_label,
  "FAIL_dn_le_150",         "Display Name is greater than 150 characters",
  "FAIL_desc_nna",          "Product Details/Short Description Missing",
  "FAIL_cat_nna",           "Google Product Category Missing",
  "FAIL_price_low_enough",  "Price greater than $10000"
)

dat %>%
  group_by(robs_vendor_name) %>%
  select(-any_of("GMCRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("GMCRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("GMCRA_")) %>% 
  rename_with(~{str_remove(.x, "GMCRA_")}) %>%
  select(robs_vendor_name, `Total Records`, any_of(gmc_assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(gmc_assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

run_report(gmc_readiness_template, ns_dat_list[[3]], paste0("## ", names(ns_dat_list)[3]))

3.3 Live Dropship Items

gmc_assessment_name_lookup_df <- tribble(
  ~tidied_name,             ~table_label,
  "FAIL_dn_le_150",         "Display Name is greater than 150 characters",
  "FAIL_desc_nna",          "Product Details/Short Description Missing",
  "FAIL_cat_nna",           "Google Product Category Missing",
  "FAIL_price_low_enough",  "Price greater than $10000"
)

dat %>%
  group_by(robs_vendor_name) %>%
  select(-any_of("GMCRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("GMCRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("GMCRA_")) %>% 
  rename_with(~{str_remove(.x, "GMCRA_")}) %>%
  select(robs_vendor_name, `Total Records`, any_of(gmc_assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(gmc_assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

run_report(gmc_readiness_template, ns_dat_list[[4]], paste0("## ", names(ns_dat_list)[4]))

3.4 Live Dealer Items

gmc_assessment_name_lookup_df <- tribble(
  ~tidied_name,             ~table_label,
  "FAIL_dn_le_150",         "Display Name is greater than 150 characters",
  "FAIL_desc_nna",          "Product Details/Short Description Missing",
  "FAIL_cat_nna",           "Google Product Category Missing",
  "FAIL_price_low_enough",  "Price greater than $10000"
)

dat %>%
  group_by(robs_vendor_name) %>%
  select(-any_of("GMCRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("GMCRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("GMCRA_")) %>% 
  rename_with(~{str_remove(.x, "GMCRA_")}) %>%
  select(robs_vendor_name, `Total Records`, any_of(gmc_assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(gmc_assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

run_report(gmc_readiness_template, ns_dat_list[[5]], paste0("## ", names(ns_dat_list)[5]))

3.5 Items in Progress

gmc_assessment_name_lookup_df <- tribble(
  ~tidied_name,             ~table_label,
  "FAIL_dn_le_150",         "Display Name is greater than 150 characters",
  "FAIL_desc_nna",          "Product Details/Short Description Missing",
  "FAIL_cat_nna",           "Google Product Category Missing",
  "FAIL_price_low_enough",  "Price greater than $10000"
)

dat %>%
  group_by(robs_vendor_name) %>%
  select(-any_of("GMCRA_prob_list")) %>% 
  summarise(
    `Total Records` = n(),
    across(contains("GMCRA_"), ~{sum(!.x)})
    ) %>%
  rename_with(~{paste0("FAIL_", .x)}, .cols = contains("GMCRA_")) %>% 
  rename_with(~{str_remove(.x, "GMCRA_")}) %>%
  select(robs_vendor_name, `Total Records`, any_of(gmc_assessment_name_lookup_df$tidied_name)) %>% 
  column_to_rownames("robs_vendor_name") %>%
  rename_via_table(gmc_assessment_name_lookup_df) %>% 
  datatable(
    colnames = c('Vendor' = 1),
    filter = "top",
    extensions = 'FixedColumns',
    options = list(
        scrollX = Manufacturer65,
        fixedColumns = Manufacturer65,
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis")
      )
    )

# run_report(gmc_readiness_template, ns_dat_list[[6]], paste0("## ", names(ns_dat_list)[6]))
# run_report(gmc_readiness_template, ns_dat_list[[6]], paste0("## ", names(ns_dat_list)[7]))
# run_report(gmc_readiness_template, ns_dat_list[[6]], paste0("## ", names(ns_dat_list)[8]))

4 Pricing Margins

Note: dealer fulfilled products are excluded from this section.

4.1 Formula for Pre Rebate Gross Profit Percent (PRGPP)

(Sale Price - Purchase Price)/Sale Price,

where: Sale Price = MAP Price or Base Price, whichever is populated for the product, and Sale Price = Base Price if both are present.

i.e. the mean is the average of each individual product’s margin percent.

4.2 Formula for After Rebate Gross Profit Percent (ARGPP)

(Sale Price - Purchase Price + CDGR)/Sale Price,

where CDGR = Cash Discount $ + Guaranteed Rebate $ and Sale Price is defined as above.

*Note that this reports treats empty values in NetSuite for Cash Discount $ and Guaranteed Rebate $ as zeroes.

4.3 Tables

run_report(pricing_margins_template, ns_dat_list[[1]], paste0("### ", names(ns_dat_list)[1]))

4.3.1 All of NetSuite

dat %>%
  filter(dealer_count == 0) %>% 
  group_by(robs_vendor_name) %>%
  summarise(
    mean_PRGPP = mean(pre_rebate_GPP, na.rm = T),
    min_PRGPP = min(pre_rebate_GPP, na.rm = T),
    max_PRGPP = max(pre_rebate_GPP, na.rm = T),
    mean_ARGPP = mean(after_rebate_GPP, na.rm = T),
    min_ARGPP = min(after_rebate_GPP, na.rm = T),
    max_ARGPP = max(after_rebate_GPP, na.rm = T)
  ) %>%
  datatable(
    rownames = F,
    filter = "top",
    editable = T,
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        # pageLength = 25,
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(list(className = 'dt-center', targets = 1))
      )
  ) %>%
  formatPercentage(c("mean_PRGPP", "min_PRGPP", "max_PRGPP"), 1) %>%
  formatPercentage(c("mean_ARGPP", "min_ARGPP", "max_ARGPP"), 1)

run_report(pricing_margins_template, ns_dat_list[[2]], paste0("### ", names(ns_dat_list)[2]))

4.3.2 Live Items

dat %>%
  filter(dealer_count == 0) %>% 
  group_by(robs_vendor_name) %>%
  summarise(
    mean_PRGPP = mean(pre_rebate_GPP, na.rm = T),
    min_PRGPP = min(pre_rebate_GPP, na.rm = T),
    max_PRGPP = max(pre_rebate_GPP, na.rm = T),
    mean_ARGPP = mean(after_rebate_GPP, na.rm = T),
    min_ARGPP = min(after_rebate_GPP, na.rm = T),
    max_ARGPP = max(after_rebate_GPP, na.rm = T)
  ) %>%
  datatable(
    rownames = F,
    filter = "top",
    editable = T,
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        # pageLength = 25,
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(list(className = 'dt-center', targets = 1))
      )
  ) %>%
  formatPercentage(c("mean_PRGPP", "min_PRGPP", "max_PRGPP"), 1) %>%
  formatPercentage(c("mean_ARGPP", "min_ARGPP", "max_ARGPP"), 1)

run_report(pricing_margins_template, ns_dat_list[[3]], paste0("### ", names(ns_dat_list)[3]))

4.3.3 Live Dropship Items

dat %>%
  filter(dealer_count == 0) %>% 
  group_by(robs_vendor_name) %>%
  summarise(
    mean_PRGPP = mean(pre_rebate_GPP, na.rm = T),
    min_PRGPP = min(pre_rebate_GPP, na.rm = T),
    max_PRGPP = max(pre_rebate_GPP, na.rm = T),
    mean_ARGPP = mean(after_rebate_GPP, na.rm = T),
    min_ARGPP = min(after_rebate_GPP, na.rm = T),
    max_ARGPP = max(after_rebate_GPP, na.rm = T)
  ) %>%
  datatable(
    rownames = F,
    filter = "top",
    editable = T,
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        # pageLength = 25,
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(list(className = 'dt-center', targets = 1))
      )
  ) %>%
  formatPercentage(c("mean_PRGPP", "min_PRGPP", "max_PRGPP"), 1) %>%
  formatPercentage(c("mean_ARGPP", "min_ARGPP", "max_ARGPP"), 1)

run_report(pricing_margins_template, ns_dat_list[[4]], paste0("### ", names(ns_dat_list)[4]))

4.3.4 Live Dealer Items

dat %>%
  filter(dealer_count == 0) %>% 
  group_by(robs_vendor_name) %>%
  summarise(
    mean_PRGPP = mean(pre_rebate_GPP, na.rm = T),
    min_PRGPP = min(pre_rebate_GPP, na.rm = T),
    max_PRGPP = max(pre_rebate_GPP, na.rm = T),
    mean_ARGPP = mean(after_rebate_GPP, na.rm = T),
    min_ARGPP = min(after_rebate_GPP, na.rm = T),
    max_ARGPP = max(after_rebate_GPP, na.rm = T)
  ) %>%
  datatable(
    rownames = F,
    filter = "top",
    editable = T,
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        # pageLength = 25,
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(list(className = 'dt-center', targets = 1))
      )
  ) %>%
  formatPercentage(c("mean_PRGPP", "min_PRGPP", "max_PRGPP"), 1) %>%
  formatPercentage(c("mean_ARGPP", "min_ARGPP", "max_ARGPP"), 1)

run_report(pricing_margins_template, ns_dat_list[[5]], paste0("### ", names(ns_dat_list)[5]))

4.3.5 Items in Progress

dat %>%
  filter(dealer_count == 0) %>% 
  group_by(robs_vendor_name) %>%
  summarise(
    mean_PRGPP = mean(pre_rebate_GPP, na.rm = T),
    min_PRGPP = min(pre_rebate_GPP, na.rm = T),
    max_PRGPP = max(pre_rebate_GPP, na.rm = T),
    mean_ARGPP = mean(after_rebate_GPP, na.rm = T),
    min_ARGPP = min(after_rebate_GPP, na.rm = T),
    max_ARGPP = max(after_rebate_GPP, na.rm = T)
  ) %>%
  datatable(
    rownames = F,
    filter = "top",
    editable = T,
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        # pageLength = 25,
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(list(className = 'dt-center', targets = 1))
      )
  ) %>%
  formatPercentage(c("mean_PRGPP", "min_PRGPP", "max_PRGPP"), 1) %>%
  formatPercentage(c("mean_ARGPP", "min_ARGPP", "max_ARGPP"), 1)

run_report(pricing_margins_template, ns_dat_list[[6]], paste0("### ", names(ns_dat_list)[6]))

4.3.6 Liabilities

dat %>%
  filter(dealer_count == 0) %>% 
  group_by(robs_vendor_name) %>%
  summarise(
    mean_PRGPP = mean(pre_rebate_GPP, na.rm = T),
    min_PRGPP = min(pre_rebate_GPP, na.rm = T),
    max_PRGPP = max(pre_rebate_GPP, na.rm = T),
    mean_ARGPP = mean(after_rebate_GPP, na.rm = T),
    min_ARGPP = min(after_rebate_GPP, na.rm = T),
    max_ARGPP = max(after_rebate_GPP, na.rm = T)
  ) %>%
  datatable(
    rownames = F,
    filter = "top",
    editable = T,
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        # pageLength = 25,
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(list(className = 'dt-center', targets = 1))
      )
  ) %>%
  formatPercentage(c("mean_PRGPP", "min_PRGPP", "max_PRGPP"), 1) %>%
  formatPercentage(c("mean_ARGPP", "min_ARGPP", "max_ARGPP"), 1)

# run_report(pricing_margins_template, ns_dat_list[[6]], paste0("### ", names(ns_dat_list)[7]))

5 Not for Sale Reasons

Here are counts for how many products in this data set are flagged Not For Sale by value of Not for Sale Reason. For more detail of counts by supplier, ask the Data Architect.

run_report(nfs_reason_count_template, ns_dat_list[[1]], paste0("## ", names(ns_dat_list)[1]))

5.1 All of NetSuite

dat %>% 
  count(`Not For Sale Reason`) %>% 
  filter(!is.na(`Not For Sale Reason`)) %>% 
  datatable()

run_report(nfs_reason_count_template, ns_dat_list[[2]], paste0("## ", names(ns_dat_list)[2]))

5.2 Live Items

dat %>% 
  count(`Not For Sale Reason`) %>% 
  filter(!is.na(`Not For Sale Reason`)) %>% 
  datatable()

run_report(nfs_reason_count_template, ns_dat_list[[3]], paste0("## ", names(ns_dat_list)[3]))

5.3 Live Dropship Items

dat %>% 
  count(`Not For Sale Reason`) %>% 
  filter(!is.na(`Not For Sale Reason`)) %>% 
  datatable()

run_report(nfs_reason_count_template, ns_dat_list[[4]], paste0("## ", names(ns_dat_list)[4]))

5.4 Live Dealer Items

dat %>% 
  count(`Not For Sale Reason`) %>% 
  filter(!is.na(`Not For Sale Reason`)) %>% 
  datatable()

run_report(nfs_reason_count_template, ns_dat_list[[5]], paste0("## ", names(ns_dat_list)[5]))

5.5 Items in Progress

dat %>% 
  count(`Not For Sale Reason`) %>% 
  filter(!is.na(`Not For Sale Reason`)) %>% 
  datatable()

run_report(nfs_reason_count_template, ns_dat_list[[6]], paste0("## ", names(ns_dat_list)[6]))

5.6 Liabilities

dat %>% 
  count(`Not For Sale Reason`) %>% 
  filter(!is.na(`Not For Sale Reason`)) %>% 
  datatable()

# run_report(nfs_reason_count_template, ns_dat_list[[6]], paste0("## ", names(ns_dat_list)[7]))

6 Dealer Data In NetSuite

run_report(dealer_fulfilled_count_template, ns_dat_list[[1]], paste0("## ", names(ns_dat_list)[1]))

6.1 All of NetSuite

col_hide_targets = c()
dealer_data_long_df %>%
  filter(Name %in% dat$Name) %>% 
  filter(dealer_code != "SPECIAL") %>% 
  filter(supplier_code != "CLOSEOUT") %>% 
  # count(dealer_code, supplier_code) %>% 
  # pivot_wider(
  #   names_from = "dealer_code", 
  #   names_glue = "{dealer_code}_count",
  #   values_from = "n",
  #   values_fill = 0) %>% 
  # untabyl() %>% 
  tabyl(supplier_code, dealer_code) %>% 
  adorn_totals(where = "row") %>%
  mutate(supplier_code = str_replace(supplier_code, "Total", "_Total")) %>%
  arrange(supplier_code) %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    )

run_report(dealer_fulfilled_count_template, ns_dat_list[[2]], paste0("## ", names(ns_dat_list)[2]))

6.2 Live Items

col_hide_targets = c()
dealer_data_long_df %>%
  filter(Name %in% dat$Name) %>% 
  filter(dealer_code != "SPECIAL") %>% 
  filter(supplier_code != "CLOSEOUT") %>% 
  # count(dealer_code, supplier_code) %>% 
  # pivot_wider(
  #   names_from = "dealer_code", 
  #   names_glue = "{dealer_code}_count",
  #   values_from = "n",
  #   values_fill = 0) %>% 
  # untabyl() %>% 
  tabyl(supplier_code, dealer_code) %>% 
  adorn_totals(where = "row") %>%
  mutate(supplier_code = str_replace(supplier_code, "Total", "_Total")) %>%
  arrange(supplier_code) %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    )

# This module doesn't make sense (and breaks) for dealerless items.
# run_report(dealer_fulfilled_count_template, ns_dat_list[[3]], paste0("## ", names(ns_dat_list)[3]))
run_report(dealer_fulfilled_count_template, ns_dat_list[[4]], paste0("## ", names(ns_dat_list)[4]))

6.3 Live Dealer Items

col_hide_targets = c()
dealer_data_long_df %>%
  filter(Name %in% dat$Name) %>% 
  filter(dealer_code != "SPECIAL") %>% 
  filter(supplier_code != "CLOSEOUT") %>% 
  # count(dealer_code, supplier_code) %>% 
  # pivot_wider(
  #   names_from = "dealer_code", 
  #   names_glue = "{dealer_code}_count",
  #   values_from = "n",
  #   values_fill = 0) %>% 
  # untabyl() %>% 
  tabyl(supplier_code, dealer_code) %>% 
  adorn_totals(where = "row") %>%
  mutate(supplier_code = str_replace(supplier_code, "Total", "_Total")) %>%
  arrange(supplier_code) %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    )

run_report(dealer_fulfilled_count_template, ns_dat_list[[5]], paste0("## ", names(ns_dat_list)[5]))

6.4 Items in Progress

col_hide_targets = c()
dealer_data_long_df %>%
  filter(Name %in% dat$Name) %>% 
  filter(dealer_code != "SPECIAL") %>% 
  filter(supplier_code != "CLOSEOUT") %>% 
  # count(dealer_code, supplier_code) %>% 
  # pivot_wider(
  #   names_from = "dealer_code", 
  #   names_glue = "{dealer_code}_count",
  #   values_from = "n",
  #   values_fill = 0) %>% 
  # untabyl() %>% 
  tabyl(supplier_code, dealer_code) %>% 
  adorn_totals(where = "row") %>%
  mutate(supplier_code = str_replace(supplier_code, "Total", "_Total")) %>%
  arrange(supplier_code) %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    )

run_report(dealer_fulfilled_count_template, ns_dat_list[[6]], paste0("## ", names(ns_dat_list)[6]))

6.5 Liabilities

col_hide_targets = c()
dealer_data_long_df %>%
  filter(Name %in% dat$Name) %>% 
  filter(dealer_code != "SPECIAL") %>% 
  filter(supplier_code != "CLOSEOUT") %>% 
  # count(dealer_code, supplier_code) %>% 
  # pivot_wider(
  #   names_from = "dealer_code", 
  #   names_glue = "{dealer_code}_count",
  #   values_from = "n",
  #   values_fill = 0) %>% 
  # untabyl() %>% 
  tabyl(supplier_code, dealer_code) %>% 
  adorn_totals(where = "row") %>%
  mutate(supplier_code = str_replace(supplier_code, "Total", "_Total")) %>%
  arrange(supplier_code) %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets),
          list(width = "20%", targets = 0)
          )
      )
    )

# run_report(dealer_fulfilled_count_template, ns_dat_list[[6]], paste0("## ", names(ns_dat_list)[7]))

7 Pre-NetSuite Dealer Data

knitr::knit_child(pre_netsuite_dealer_report, envir = environment(), quiet = Manufacturer65) %>% cat

col_hide_targets = c()

pns_dv_df %>% 
  mutate(dealer_code = str_sub(dealer_sku, 1, 4)) %>% 
  count(dealer_code, match_status) %>% 
  pivot_wider(
    names_from = match_status,
    values_from = n,
    values_fill = 0
  ) %>% 
  enrich_join(dealer_metadata_df, dealer_code, `Dealer name`) %>% 
  mutate(Dealer = word(`Dealer name`)) %>% 
  select(Dealer, everything()) %>% 
  select(-dealer_code, - `Dealer name`) %>% 
  adorn_totals(where = "row") %>%
  mutate(Total = select(., where(is.numeric)) %>%
         rowSums(na.rm = Manufacturer65)) %>% 
  datatable(
    rownames = F,
    filter = "top",
    extensions = "Buttons",
    options = list(
        dom = 'Blfrtip',
        buttons = c("copy", "excel", "colvis"),
        pageLength = 15,
        columnDefs = list(
          list(extend = 'colvis', visible = F, targets = col_hide_targets)
          # list(width = "20%", targets = 0)
          )
      )
    )